gavindiaz/NexQuant
1
0
Fork 0
mirror of https://github.com/NicolasBohn/NexQuant.git synced 2026-08-02 09:57:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

use popover instead of expander (#1138)

Browse Source
This commit is contained in:
XianBW
2025-07-31 18:14:38 +08:00
committed by GitHub
parent 6b0fae5000
commit 692f3006ce
1 changed files with 30 additions and 22 deletions
Download Patch File Download Diff File Expand all files Collapse all files
rdagent/log/ui/ds_trace.py
+30 -22
View File
@@ -189,9 +189,11 @@ def workspace_win(workspace, cmp_workspace=None, cmp_name="last code."):
if len(show_files) > 0:
if cmp_workspace:
diff = generate_diff_from_dict(cmp_workspace.file_dict, show_files, "main.py")
with st.expander(f":violet[**Diff with {cmp_name}**]"):
with st.popover(f":violet[**Diff with {cmp_name}**]", use_container_width=True, icon="🔍"):
st.code("".join(diff), language="diff", wrap_lines=True, line_numbers=True)
with st.expander(f"Files in :blue[{replace_ep_path(workspace.workspace_path)}]"):
with st.popover(
f"Files in :blue[{replace_ep_path(workspace.workspace_path)}]", use_container_width=True, icon="📂"
):
code_tabs = st.tabs(show_files.keys())
for ct, codename in zip(code_tabs, show_files.keys()):
with ct:
@@ -562,6 +564,8 @@ def replace_ep_path(p: Path):
def get_llm_call_stats(llm_data: dict) -> tuple[int, int]:
total_llm_call = 0
total_filter_call = 0
total_call_seconds = 0
filter_call_seconds = 0
filter_sys_prompt = T("rdagent.utils.prompts:filter_redundant_text.system").r()
for li, loop_d in llm_data.items():
for fn, loop_fn_d in loop_d.items():
@@ -569,9 +573,12 @@ def get_llm_call_stats(llm_data: dict) -> tuple[int, int]:
for d in v:
if "debug_llm" in d["tag"]:
total_llm_call += 1
total_call_seconds += d["obj"].get("duration", 0)
if "system" in d["obj"] and filter_sys_prompt == d["obj"]["system"]:
total_filter_call += 1
return total_llm_call, total_filter_call
filter_call_seconds += d["obj"].get("duration", 0)
return total_llm_call, total_filter_call, total_call_seconds, filter_call_seconds
def get_timeout_stats(llm_data: dict):
@@ -622,18 +629,23 @@ def summarize_win():
with info3.popover("RDLOOP", icon="⚙️"):
st.write(state.data.get("settings", {}).get("RDLOOP_SETTINGS", "No settings found."))
llm_call, llm_filter_call = get_llm_call_stats(state.llm_data)
info4.metric("LLM Calls", llm_call)
info5.metric("LLM Filter Calls", f"{llm_filter_call}({round(llm_filter_call / llm_call * 100, 2)}%)")
llm_call, llm_filter_call, llm_call_seconds, llm_filter_call_seconds = get_llm_call_stats(state.llm_data)
info4.metric("LLM Calls", llm_call, help=timedelta_to_str(timedelta(seconds=llm_call_seconds)))
info5.metric(
"LLM Filter Calls",
llm_filter_call,
delta=-round(llm_filter_call / llm_call, 5),
help=timedelta_to_str(timedelta(seconds=llm_filter_call_seconds)),
)
timeout_stats = get_timeout_stats(state.llm_data)
info6.metric(
"Timeouts (Coding)",
"Timeouts (C)",
f"{round(timeout_stats['coding']['timeout'] / timeout_stats['coding']['total'] * 100, 2)}%",
help=f"{timeout_stats['coding']['timeout']}/{timeout_stats['coding']['total']}",
)
info7.metric(
"Timeouts (Running)",
"Timeouts (R)",
f"{round(timeout_stats['running']['timeout'] / timeout_stats['running']['total'] * 100, 2)}%",
help=f"{timeout_stats['running']['timeout']}/{timeout_stats['running']['total']}",
)
@@ -661,8 +673,8 @@ def summarize_win():
"Running Score (valid)",
"Running Score (test)",
"Feedback",
"e-loops(coding)",
"e-loops(running)",
"e-loops(c)",
"e-loops(r)",
"COST($)",
"Time",
"Exp Gen",
@@ -784,18 +796,14 @@ def summarize_win():
if "coding" in loop_data:
if len([i for i in loop_data["coding"].keys() if isinstance(i, int)]) == 0:
df.loc[loop, "e-loops(coding)"] = 0
df.loc[loop, "e-loops(c)"] = 0
else:
df.loc[loop, "e-loops(coding)"] = (
max(i for i in loop_data["coding"].keys() if isinstance(i, int)) + 1
)
df.loc[loop, "e-loops(c)"] = max(i for i in loop_data["coding"].keys() if isinstance(i, int)) + 1
if "running" in loop_data:
if len([i for i in loop_data["running"].keys() if isinstance(i, int)]) == 0:
df.loc[loop, "e-loops(running)"] = 0
df.loc[loop, "e-loops(r)"] = 0
else:
df.loc[loop, "e-loops(running)"] = (
max(i for i in loop_data["running"].keys() if isinstance(i, int)) + 1
)
df.loc[loop, "e-loops(r)"] = max(i for i in loop_data["running"].keys() if isinstance(i, int)) + 1
if "feedback" in loop_data:
fb_emoji_str = "✅" if bool(loop_data["feedback"]["no_tag"]) else "❌"
if sota_loop_id == loop:
@@ -863,7 +871,7 @@ def summarize_win():
total_num = x.shape[0]
valid_num = x[x["Running Score (test)"] != "N/A"].shape[0]
success_num = x[x["Feedback"] == "✅"].shape[0]
avg_e_loops = x["e-loops(coding)"].mean()
avg_e_loops = x["e-loops(c)"].mean()
return pd.Series(
{
"Loop Num": total_num,
@@ -871,7 +879,7 @@ def summarize_win():
"Success Loop": success_num,
"Valid Rate": round(valid_num / total_num * 100, 2),
"Success Rate": round(success_num / total_num * 100, 2),
"Avg e-loops(coding)": round(avg_e_loops, 2),
"Avg e-loops(c)": round(avg_e_loops, 2),
}
)
@@ -879,7 +887,7 @@ def summarize_win():
# component statistics
comp_df = (
df.loc[:, ["Component", "Running Score (test)", "Feedback", "e-loops(coding)"]]
df.loc[:, ["Component", "Running Score (test)", "Feedback", "e-loops(c)"]]
.groupby("Component")
.apply(comp_stat_func, include_groups=False)
)
@@ -892,7 +900,7 @@ def summarize_win():
)
comp_df["Valid Rate"] = comp_df["Valid Rate"].apply(lambda x: f"{x}%")
comp_df["Success Rate"] = comp_df["Success Rate"].apply(lambda x: f"{x}%")
comp_df.loc["Total", "Avg e-loops(coding)"] = round(df["e-loops(coding)"].mean(), 2)
comp_df.loc["Total", "Avg e-loops(c)"] = round(df["e-loops(c)"].mean(), 2)
st2.markdown("### Component Statistics")
st2.dataframe(comp_df)